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A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers
The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers.
Exponentially Many Local Minima in Quantum Neural Networks
Specifically, we show for typical under-parameterized QNNs, there exists a dataset that induces a loss function with the number of spurious local minima depending exponentially on the number of parameters.
Quantum exploration algorithms for multi-armed bandits
Identifying the best arm of a multi-armed bandit is a central problem in bandit optimization.
On Second-Order Group Influence Functions for Black-Box Predictions
Often we want to identify an influential group of training samples in a particular test prediction for a given machine learning model.
Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares
We propose a Slicing Initialized Alternating Subspace Iteration (s-ASI) method that is guaranteed to recover top $r$ components ($\epsilon$-close) simultaneously for (a)symmetric tensors almost surely under the noiseless case (with high probability for a bounded noise) using $O(\log(\log \frac{1}{\epsilon}))$ steps of tensor subspace iterations.
